Nitish Singh
Nitish Singh is a skilled data scientist with over a decade of experience in AI, machine learning, and generative AI. Currently at Red Hat, he focuses on leveraging advanced AI technologies to drive innovation and deliver impactful business insights. Nitish has expertise in predictive analytics, NLP, and deploying AI solutions on scalable cloud platforms.
An MBA graduate in Marketing & IT from the Institute of Management, Ahmedabad, and a B.Tech in Electronics & Communication from SKIT Jaipur, Nitish has been recognized for his contributions with multiple awards, including accolades for innovation and excellence in AI-driven projects
RedHat
Job title –Senior Data Scientist
Session
Generative AI has revolutionized text processing, but quantitative datasets pose unique challenges, such as high variability, limited historical data, and complex relationships. Traditional models, including LLMs, often fail to deliver precise and actionable insights for such data types.
Enter Large Quantitative Models (LQMs)
LQMs combine the strengths of Variational AutoEncoders (VAEs) and Generative Adversarial Networks (GANs) to address these challenges. By learning latent structures and generating synthetic data, LQMs enhance predictive accuracy and robustness, bridging critical gaps in data-limited environments.
Beyond Specification: A Universal Tool
While originally designed for financial forecasting, LQMs have versatile applications. From improving IoT sensor predictions to simulating patient outcomes in healthcare, these models bring reliability and adaptability across domains.
A Glimpse Into the Future
This talk explores the brief of architecture and applications of LQMs.Audience will leave with a new perspective on generative AI’s potential for quantitative analysis and actionable steps to experiment with LQMs using open-source tools.